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Relevant bibliographies by topics / Normalizing transformation

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Academic literature on the topic 'Normalizing transformation'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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Journal articles on the topic "Normalizing transformation"

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Hong, Seongmin, and Se Young Chun. "Neural Diffeomorphic Non-uniform B-spline Flows." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 12225–33. http://dx.doi.org/10.1609/aaai.v37i10.26441.

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Normalizing flows have been successfully modeling a complex probability distribution as an invertible transformation of a simple base distribution. However, there are often applications that require more than invertibility. For instance, the computation of energies and forces in physics requires the second derivatives of the transformation to be well-defined and continuous. Smooth normalizing flows employ infinitely differentiable transformation, but with the price of slow non-analytic inverse transforms. In this work, we propose diffeomorphic non-uniform B-spline flows that are at least twice

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Prykhodko, Sergiy, Ivan Shutko, and Andrii Prykhodko. "Early LOC Estimation of Web Apps Created Using Yii Framework by Nonlinear Regression Models." WSEAS TRANSACTIONS ON COMPUTERS 20 (November 23, 2021): 321–28. http://dx.doi.org/10.37394/23205.2021.20.35.

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We have performed early LOC estimation of Web applications (apps) created using the Yii framework by three nonlinear regression models with three predictors based on the normalizing transformations. We used two univariate transformations (the decimal logarithm and the Box-Cox transformation) and the Box-Cox four-variate transformation for constructing nonlinear regression models. The nonlinear regression model constructed by the Box-Cox four-variate transformation has better size prediction results compared to other regression ones based on the univariate transformations.

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Viveros, R. "An Approximate Normalizing Power Transformation for theFDistribution." Communications in Statistics - Simulation and Computation 19, no. 1 (1990): 57–69. http://dx.doi.org/10.1080/03610919008812845.

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Prykhodko, S. B., I. S. Shutko, and A. S. Prykhodko. "A NONLINEAR REGRESSION MODEL TO ESTIMATE THE SIZE OF WEB APPS CREATED USING THE CAKEPHP FRAMEWORK." Radio Electronics, Computer Science, Control, no. 4 (January 13, 2022): 129–39. http://dx.doi.org/10.15588/1607-3274-2021-4-12.

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Context. The problem of estimating the software size in the early stage of a software project is important because a software size estimate is used for predicting the software development effort, including Web apps created using the CakePHP framework. The object of the study is the process of estimating the size of Web apps created using the CakePHP framework. The subject of the study is the nonlinear regression models to estimate the size of Web apps created using the CakePHP framework. Objective. The goal of the work is the building the nonlinear regression model with three predictors

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Prykhodko, S. B., N. V. Prykhodko, and A. V. Koltsov. "A NONLINEAR REGRESSION MODEL FOR EARLY LOC ESTIMATION OF OPEN-SOURCE KOTLIN-BASED APPLICATIONS." Radio Electronics, Computer Science, Control, no. 1 (April 2, 2024): 85. http://dx.doi.org/10.15588/1607-3274-2024-1-8.

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Context. The early lines of code (LOC) estimation in software projects holds significant importance, as it directly influences the prediction of development effort, covering a spectrum of different programming languages, and open-source Kotlin-based applications in particular. The object of the study is the process of early LOC estimation of open-source Kotlin-based apps. The subject of the study is the nonlinear regression models for early LOC estimation of open-source Kotlin-based apps. Objective. The goal of the work is to build the nonlinear regression model with three predictors for early

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He, Zhi Jun, Jing Li, and Jin Xin Liu. "Influence on the Microstructure of Pinion Steel with Different Cooling Rates after Normalizing." Applied Mechanics and Materials 513-517 (February 2014): 12–15. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.12.

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According to continuous cooling transformation curve of pinion steel, heat treatment simulation tests of 8 series were developed. The results show that the microstructure of pinion steel depends on the cooling rate after normalizing. Furnace cooling according to 30°C/h after normalizing can get the typical F+P organizations; Converter cooling after normalizing mixed with some bainite organizations; Furnace cooling with the break after normalizing can get clear stripped bainite organizations.

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Fukasawa, Masaaki. "THE NORMALIZING TRANSFORMATION OF THE IMPLIED VOLATILITY SMILE." Mathematical Finance 22, no. 4 (2011): 753–62. http://dx.doi.org/10.1111/j.1467-9965.2011.00483.x.

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Taneichi, Nobuhiro, Yuri Sekiya, Akio Suzukawa, and Hideyuki Imai. "A FORMULA FOR NORMALIZING TRANSFORMATION OF SOME STATISTICS." Communications in Statistics - Theory and Methods 31, no. 2 (2002): 163–79. http://dx.doi.org/10.1081/sta-120002643.

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Prykhodko, Sergiy, Natalia Prykhodko, and Kateryna Knyrik. "Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression Analysis." Applied Computer Systems 25, no. 2 (2020): 172–79. http://dx.doi.org/10.2478/acss-2020-0019.

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Abstract The authors consider the construction of a nonlinear multiple regression model, its confidence and prediction intervals to evaluate the efforts of mobile application development in the planning phase based on the multivariate normalizing transformation and outlier detection. The constructed model is compared to the linear regression model and nonlinear regression models based on the univariate transformations, such as the decimal logarithm, Box–Cox, and Johnson transformation. This model, in comparison with other regression models, has better prediction accuracy.

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Li, Huaixiang, and D. B. Owen. "On the umvu estimators after using a normalizing transformation." Communications in Statistics - Theory and Methods 18, no. 3 (1989): 801–16. http://dx.doi.org/10.1080/03610928908829935.

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Dissertations / Theses on the topic "Normalizing transformation"

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Newman, Christian D. "NORMALIZING-REFACTORINGS: SIMPLIFYING THE CONSTRUCTION OF SOURCE CODE TRANSFORMATIONS." Kent State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=kent1385057030.

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Prykhodko, S., N. Prykhodko, L. Makarova, O. Kudin, T. Smykodub, and A. Prykhodko. "Detecting bivariate outliers on the basis of normalizing transformations for non-Gaussian data." Thesis, Sumy State University, 2017. http://essuir.sumdu.edu.ua/handle/123456789/55754.

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The statistical technique for detecting outliers in bivariate non-Gaussian data on the basis of normalizing transformations, prediction ellipse and a test statistic (TS) for the Mahalanobis squared distance (MSD), which has an approximate F distribution, is proposed. Application of the technique is considered for detecting outliers in two bivariate non-Gaussian data sets: the first, actual effort (hours) and size (adjusted function points) from 145 maintenance and development projects, the second, effort (hours) and mass (tonnes) of designed the section of the ship from 188 designs

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Books on the topic "Normalizing transformation"

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Rottenberg, Catherine. How Superwoman Became Balanced. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190901226.003.0002.

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This chapter tracks the cultural transformation in conceptions of “progress” for middle-class women in the United States, as well as how liberal feminism has undergone a reorientation: from struggling for equality to advocating balance and happiness. Examining Anne-Marie Slaughter’s famous Atlantic article “Why Women Still Can’t Have It All” in conjunction with popular television series such as The Good Wife, the chapter argues that progress has come to signify women’s ability to successfully balance work with family. This transformation is the result of an intensifying crisis in liberalism’s

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Colombo, Agustín, Edward McGushin, and Geoff Pfeifer. Politics of Desire. The Rowman & Littlefield Publishing Group, 2022. https://doi.org/10.5040/9798881818296.

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In his preface to Deleuze and Guattari’s Anti-Oedipus, Michel Foucault notes that in the late sixties, there is a turn away from Freud anda movement toward what he calls an “experience and technology of desire that is no longer Freudian”. Foucault, Deleuze, and Guattari were interested in, and engaged with this shift and their collective work in these areas spawned a larger post-Freudian literature. This book gathers contributions from international scholars with the aim of exploring the social, political, and philosophical dimension of Deleuze and Guattari’s, and Foucault’s critical encounter

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Nunes, Mark, and Cassandra Ozog, eds. “You’re Muted”. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9798765108260.

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Through the frame of Zoom, this collection of essays examines the rapid emergence of videoconferencing in everyday life under COVID-19, its preexisting performative logic, and the ongoing implication of these practices for millions of individuals and institutions. The year 2023 marked the end of the World Health Organization’s classification of the COVID-19 outbreak as a “public health emergency of international concern,” yet many of the organizational and institutional restructurings that occurred in the rapid response to the pandemic have remained firmly in place. The prevalence of videoconf

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Book chapters on the topic "Normalizing transformation"

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Yang, Canming. "An Analysis of the Causes of Disorders in China’s Income Distribution: From the Perspective of Economic Transformation." In Research on Normalizing Income Distribution Order in China. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1886-5_7.

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Shore, Haim. "Process Control for Non-Normal Populations Based on an Inverse Normalizing Transformation." In Frontiers in Statistical Quality Control 6. Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-642-57590-7_12.

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Broer, Henk, Igor Hoveijn, Gerton Lunter, and Gert Vegter. "7. Computing normalizing transformations." In Lecture Notes in Mathematics. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36398-9_7.

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Billi, Luca, Monica Malavasi, and Giorgio Turchetti. "Natural Boundaries of Normalizing Transformations." In Hamiltonian Mechanics. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-0964-0_3.

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Bazzani, A., and G. Turchetti. "Analyticty of Normalizing Transformations for Area Preserving Maps." In Seminar on Dynamical Systems. Birkhäuser Basel, 1994. http://dx.doi.org/10.1007/978-3-0348-7515-8_12.

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Schwichtenberg, Simon, Christian Gerth, Zille Huma, and Gregor Engels. "Normalizing Heterogeneous Service Description Models with Generated QVT Transformations." In Modelling Foundations and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09195-2_12.

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Rukhin, Andrew L. "Estimating Quadratic Polynomials with Applications to Square Root Normalizing Transformations." In Mathematical Statistics and Probability Theory. Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3965-3_19.

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Bazzani, Armando, and Giorgio Turchetti. "Perturbation Theory and Analyticity of Normalizing Transformations for Area Preserving Maps." In NATO ASI Series. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3464-8_7.

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Prykhodko, Sergiy, and Natalia Prykhodko. "Mathematical Modeling of Non-Gaussian Dependent Random Variables by Nonlinear Regression Models Based on the Multivariate Normalizing Transformations." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58124-4_16.

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Shore, Haim. "Inverse Normalizing Transformations and An Extended Normalizing Transformation." In Advances on Theoretical and Methodological Aspects of Probability and Statistics. CRC Press, 2003. http://dx.doi.org/10.1201/9780203493205.ch9.

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Conference papers on the topic "Normalizing transformation"

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Frame, Lesley D. "In-Line Induction Heat Treating of Tube, Pip, and Bar Products." In HT 2013, edited by B. Lynn Ferguson. ASM International, 2013. https://doi.org/10.31399/asm.cp.ht2013p0096.

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Abstract Requirements for greater product flexibility in the heat treating industry, driven by lean strategies, have resulted in growing global capacity for induction heating operations as well as the re-examination of processes to save production time and line space. However, there are important considerations to keep in mind when developing and modifying heat treat recipes whether for full-body, full-length heat treating, or seam normalizing applications. These include consideration of specialty alloy requirements and the presence of micro-alloying elements, the prior microstructure and pres

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Edneral, Victor F. "Computer generation of normalizing transformation for systems of nonlinear ODE." In the 1993 international symposium. ACM Press, 1993. http://dx.doi.org/10.1145/164081.164085.

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Huang, Zhizhong, Shouzhen Chen, Junping Zhang, and Hongming Shan. "AgeFlow: Conditional Age Progression and Regression with Normalizing Flows." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/103.

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Age progression and regression aim to synthesize photorealistic appearance of a given face image with aging and rejuvenation effects, respectively. Existing generative adversarial networks (GANs) based methods suffer from the following three major issues: 1) unstable training introducing strong ghost artifacts in the generated faces, 2) unpaired training leading to unexpected changes in facial attributes such as genders and races, and 3) non-bijective age mappings increasing the uncertainty in the face transformation. To overcome these issues, this paper proposes a novel framework, termed AgeF

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Mao, Aihua, Yaqi Duan, Yu-Hui Wen, Zihui Du, Hongmin Cai, and Yong-Jin Liu. "Invertible Residual Neural Networks with Conditional Injector and Interpolator for Point Cloud Upsampling." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/141.

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Point clouds obtained by LiDAR and other sensors are usually sparse and irregular. Low-quality point clouds have serious influence on the final performance of downstream tasks. Recently, a point cloud upsampling network with normalizing flows has been proposed to address this problem. However, the network heavily relies on designing specialized architectures to achieve invertibility. In this paper, we propose a novel invertible residual neural network for point cloud upsampling, called PU-INN, which allows unconstrained architectures to learn more expressive feature transformations. Then, we p

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Marshall, Michael, and Martin Fingerhut. "Reframing PSMS in the Context of Operational Risk Management and ESG Sustainability." In 2022 14th International Pipeline Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/ipc2022-87773.

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Abstract Current Environmental, Health, and Safety (EHS) and Asset Integrity/Performance Management (AIM/APM) platforms fail to properly aggregate data from multiple Pipeline Safety Management System (PSMS) workflows into a single database which can be queried to inform real-time, risk-based analyses and decision-making relative to profitability impacts. With predictive analytics at the core of an asset integrity and PSMS framework intent upon achieving holistic, enterprise-wide visibility and accountability for Environmental, Social, and Governance (ESG) program effectiveness and sustainabili

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Oriekhov, Oleksandr, Tetyana Farionova, Liubava Chernova, Lyudmila Chernova, and Mykhaіlo Vorona. "Nonlinear regression models for software size estimation of Data Science and Machine Learning Java-applications." In IT Project Management 2024. CEUR Workshop Proceedings, 2024. https://doi.org/10.23939/iw_itpm2024.054.

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his paper introduces the usage of regression models and equations for Data Science and Machine Learning Java applications size estimation. Size estimation of applications plays one of the key planning tasks at the early stages of project planning for the successful implementation of software development projects. Application size estimation is used to predict software development effort estimation using parametric models such as COCOMO, COCOMO II, etc. The aim of the study is to increase the reliability and accuracy of size estimation of Data Science and Machine Learning Java applications at t

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Prykhodko, Sergiy, Natalia Prykhodko, Lidiia Makarova, and Andrii Pukhalevych. "Outlier Detection in Non-Linear Regression Analysis Based on the Normalizing Transformations." In 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). IEEE, 2020. http://dx.doi.org/10.1109/tcset49122.2020.235464.

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Prykhodko, Sergiy, Natalia Prykhodko, Lidiia Makarova, and Kateryna Pugachenko. "Detecting outliers in multivariate non-Gaussian data on the basis of normalizing transformations." In 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON). IEEE, 2017. http://dx.doi.org/10.1109/ukrcon.2017.8100366.

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Prykhodko, Sergiy, Natalia Prykhodko, and Lidiia Makarova. "Building the Non-Linear Regression Equations on the Basis of Multivariate Normalizing Transformations." In 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC). IEEE, 2018. http://dx.doi.org/10.1109/saic.2018.8516742.

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Zhang, P., T. Gao, R. Li, and J. Fu. "Advanced Machine Learning Framework for Enhanced Lithology Classification and Identification." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23595-ms.

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Abstract In this comprehensive study, machine learning (ML) techniques are employed to revolutionize lithology classification within the geosciences, emphasizing the transformative impact of ML on traditional practices. The research encapsulates ML's integration into well-log data analysis, enhancing prediction accuracy and efficiency in lithology identification—a crucial aspect of subsurface exploration. The methodology adopted includes systematic data preprocessing, feature extraction, and the deployment of advanced ML algorithms such as Support Vector Machines and Random Forest for litholog

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Reports on the topic "Normalizing transformation"

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Taniguchi, M., P. R. Krishnaiah, and R. Chao. Normalizing Transformations of Some Statistics of Gaussian ARMA Processes. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada170184.

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